Analyze abaza stress
2022-05-10
Достать данные
Данные представляют собой аннотированные слова, записанные от 6 носителей в селении Красный Восток:
ls raw_data## d23_stress.TextGrid
## d23_stress.wav
## d25_stress.TextGrid
## d25_stress.wav
## d26_stress.TextGrid
## d26_stress.wav
## d27_stress.TextGrid
## d27_stress.wav
## d28_stress.TextGrid
## d28_stress.wav
## d30_stress.TextGrid
## d30_stress.wavОт каждого носителя было записано от 74 до 79 стимульных слов:
library(phonfieldwork)
textgrids <- read_from_folder("raw_data", "textgrid")
textgrids %>%
filter(tier == 1) %>%
distinct(source, content) %>%
count(source)Чего мы удалили? Про это нужно написать.
textgrids %>%
filter(tier_name %in% c("labels", "error")) %>%
select(-tier, -id) %>%
pivot_wider(names_from = tier_name, values_from = content) %>%
mutate(speaker = str_remove(source, ".TextGrid"),
word = labels) %>%
filter(!is.na(error), error != "") %>%
select(speaker, word) ->
to_remove
to_removeДальше следует описание данных, см. Appendix
draw_sound(file_name = "raw_data/d23_stress.wav",
annotation = "raw_data/d23_stress.TextGrid",
from = 0,
to = 10.6,
zoom = c(3.8, 4.4))
После этого мы применяем к этим данным вот этот скрипт:
cat get_pitch_intencity.praat## # This is a Praat script made for investigation of Abaza vowels. It analyses multiple selected sounds
## # (TextGrids should be also uploaded to Praat Obects). The file should have the following structure:
## # * first tier --- word label
## # * second tier --- translation label
## # * third tier --- sound label
## # * fourth tier --- utterance label
##
##
## # This script is distributed under the GNU General Public License.
## # George Moroz 09.05.2022
##
## form Get Pitch listing from a file
## comment Where should the script write a result file
## text directory /home/agricolamz/for_work/HSE/students/2022_Kuznetsova/data/
## comment How should the script name a result file
## text resultfile log.txt
## comment Time step
## real step 0.01
## comment Pitch floor (Hz)
## integer floor 90
## comment Pitch ceiling (Hz)
## integer ceiling 250
## comment 5. formant ceiling (Hz)
## integer fceiling 5500
## comment Minimum pitch for intensity (Hz)
## integer mpitch 200
## endform
##
## n = numberOfSelected("Sound")
## for j to n
## sound[j] = selected("Sound", j)
## endfor
## for k to n
## selectObject: sound[k]
## object_name$ = selected$ ("Sound")
## select TextGrid 'object_name$'
## number_of_intervals = Get number of intervals... 3
## for b from 1 to number_of_intervals
## select TextGrid 'object_name$'
## interval_label$ = Get label of interval... 3 'b'
## utterance$ = Get label of interval... 4 'b'
## if interval_label$ <> ""
## start = Get starting point... 3 'b'
## end = Get end point... 3 'b'
## duration = end - start
## int_1 = Get interval at time... 1 end
## word$ = Get label of interval... 1 int_1
## trans$ = Get label of interval... 2 int_1
## select Sound 'object_name$'
## s = Extract part: start, end, "rectangular", 1, "yes"
## select s
## fragment_name$ = selected$ ("Sound")
## pitch = To Pitch... step floor ceiling
## selectObject: s
## formant = To Formant (burg): 0, 5, fceiling, 0.025, 50
## selectObject: s
## intensity = To Intensity: mpitch, 0, "no"
## i = start
## while i <= end
## select Pitch 'fragment_name$'
## f0 = Get value at time... 'i' Hertz Linear
## select Formant 'fragment_name$'
## f1 = Get value at time: 1, i, "Hertz", "Linear"
## f2 = Get value at time: 2, i, "Hertz", "Linear"
## f3 = Get value at time: 3, i, "Hertz", "Linear"
## select Intensity 'fragment_name$'
## intvalue = Get value at time: 'i', "cubic"
## i = i + 0.01
## fileappend "'directory$''resultfile$'" 'object_name$''tab$''interval_label$''tab$''utterance$''tab$''word$''tab$''trans$''tab$''f0''tab$''f1''tab$''f2''tab$''f3''tab$''intvalue''tab$''duration''tab$''i''newline$'
## endwhile
## removeObject: s
## removeObject: pitch
## removeObject: formant
## removeObject: intensity
## endif
## endfor
## # removeObject: "Sound 'object_name$'"
## # removeObject: "TextGrid 'object_name$'"
## endforСкрипт идет по аннотации и с шагом в 1 мс и базовыми настройками,
отраженными в меню (между form и endform),
берет значения длительности, f0, f1, f2, f3 и инетнсивности. Получается
вот такая вот таблица:
df <- read_tsv("data/log.txt", col_names = FALSE)
colnames(df) <- c("speaker", "vowel", "utterance", "word", "translation", "f0", "f1", "f2", "f3", "intensity", "duration", "step")
dfДальше с данными можно делать разное, я просто возьму среднее по всем параметрами и создам переменную minimal_pair, которая будет включать минимальную пару:
df %>%
mutate_all(function(i){str_replace(i, "--undefined--", NA_character_)}) %>%
mutate(across(f0:duration, as.double),
stressed = ifelse(vowel == toupper(vowel), "stressed", "unstressed"),
stressed = factor(stressed, levels = c("unstressed", "stressed")),
vowel_n = str_extract(utterance, "V\\d"),
vowel_n = as.double(str_remove(vowel_n, "V")),
utterance = str_extract(utterance, "u\\d"),
utterance = as.double(str_remove(utterance, "u"))) %>%
filter(str_detect(vowel, "[аА]"),
utterance <= 4) %>%
select(-step) %>%
anti_join(to_remove) %>%
group_by(speaker, utterance, stressed, word, vowel_n) %>%
summarise(f0 = mean(f0, na.rm = TRUE),
f1 = mean(f1, na.rm = TRUE),
f2 = mean(f2, na.rm = TRUE),
f3 = mean(f3, na.rm = TRUE),
intensity = mean(intensity, na.rm = TRUE),
duration = mean(duration, na.rm = TRUE)) %>%
mutate(vowel_n = str_c(vowel_n, ". syllable"),
utterance = str_c(utterance, ". utterance"),
duration = duration*1000) %>%
ungroup() %>%
mutate(word_pair = as.double(factor(tolower(word)))) %>%
group_by(word_pair) %>%
mutate(minimal_pair = str_c(unique(str_c(word, "_")), collapse = ""),
minimal_pair = str_remove(minimal_pair, "_$")) %>%
ungroup() ->
mean_values
mean_valuesВы спрашивали, какие минимальные пары вошли в анализ?
mean_values %>%
distinct(speaker, word_pair, minimal_pair) %>%
pivot_wider(names_from = speaker, values_from = minimal_pair) %>%
select(-word_pair) %>%
write_csv("~/Desktop/pairs.csv")График с разницами между минимальными парами (ударный слог минус безударный). Если разницы нет, то горб должен возвышаться над 0. Если же он от нуля смещен, то значит разница между ударными и безударными слогами есть.
mean_values %>%
select(speaker, utterance, stressed, minimal_pair, vowel_n, duration) %>%
pivot_wider(names_from = stressed, values_from = duration) %>%
mutate(duration_difference = stressed-unstressed) %>%
ggplot(aes(duration_difference, fill = vowel_n))+
geom_density(alpha = 0.4)+
facet_grid(speaker~utterance, scales = "free")
mean_values %>%
select(speaker, utterance, stressed, minimal_pair, vowel_n, f0) %>%
pivot_wider(names_from = stressed, values_from = f0) %>%
mutate(f0_difference = stressed-unstressed) %>%
ggplot(aes(f0_difference, fill = vowel_n))+
geom_density(alpha = 0.4)+
facet_grid(speaker~utterance, scales = "free")
mean_values %>%
select(speaker, utterance, stressed, minimal_pair, vowel_n, intensity) %>%
pivot_wider(names_from = stressed, values_from = intensity) %>%
mutate(intensity_difference = stressed-unstressed) %>%
ggplot(aes(intensity_difference, fill = vowel_n))+
geom_density(alpha = 0.4)+
facet_grid(speaker~utterance, scales = "free")
Статистика
Я использую байесовскую логистическую регрессию со смешанными эффектами с дефолтными прайерами и формула выглядит вот так:
stressed ~ ПЕРЕМЕННАЯ * vowel_n + (1|speaker) + (1|minimal_pair/utterance)Получается взаимодействие переменной и номера гласного в слове с носителем в смешанных эффектах и номером произнесения вложенным в минимальную пару в другом смешанном эффекте.
library(brms)
mean_values %>%
select(speaker, utterance, stressed, minimal_pair, vowel_n, duration) %>%
brm(stressed ~ duration*vowel_n + (1|speaker) + (vowel_n+1|minimal_pair/utterance),
family = bernoulli(),
data = .) ->
fit_duration##
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## Chain 4:fit_duration## Family: bernoulli
## Links: mu = logit
## Formula: stressed ~ duration * vowel_n + (1 | speaker) + (vowel_n + 1 | minimal_pair/utterance)
## Data: . (Number of observations: 3398)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~minimal_pair (Number of levels: 40)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 1.30 0.19 0.99 1.72 1.00
## sd(vowel_n2.syllable) 1.21 0.20 0.86 1.63 1.00
## cor(Intercept,vowel_n2.syllable) -0.98 0.02 -1.00 -0.93 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 857 1536
## sd(vowel_n2.syllable) 1007 1823
## cor(Intercept,vowel_n2.syllable) 1296 1624
##
## ~minimal_pair:utterance (Number of levels: 160)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.35 0.12 0.09 0.57 1.01
## sd(vowel_n2.syllable) 0.14 0.11 0.00 0.40 1.00
## cor(Intercept,vowel_n2.syllable) 0.18 0.55 -0.90 0.96 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 710 616
## sd(vowel_n2.syllable) 760 1115
## cor(Intercept,vowel_n2.syllable) 2379 2051
##
## ~speaker (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.57 0.56 0.85 3.01 1.00 1438 2136
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -8.64 0.77 -10.17 -7.15 1.00 897
## duration 0.06 0.00 0.05 0.06 1.00 2906
## vowel_n2.syllable 1.33 0.47 0.42 2.23 1.00 1913
## duration:vowel_n2.syllable -0.02 0.00 -0.03 -0.01 1.00 2952
## Tail_ESS
## Intercept 1729
## duration 3207
## vowel_n2.syllable 2272
## duration:vowel_n2.syllable 2871
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot(fit_duration)
conditional_effects(fit_duration,
effects = c("duration:vowel_n"))
mean_values %>%
select(speaker, utterance, stressed, minimal_pair, vowel_n, f0) %>%
brm(stressed ~ f0*vowel_n + (1|speaker) + (vowel_n+1|minimal_pair/utterance),
family = bernoulli(),
data = .) ->
fit_f0##
## SAMPLING FOR MODEL 'e67f1fb651efe88012e69c4f524f5c99' NOW (CHAIN 1).
## Chain 1:
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## Chain 4:fit_f0## Family: bernoulli
## Links: mu = logit
## Formula: stressed ~ f0 * vowel_n + (1 | speaker) + (vowel_n + 1 | minimal_pair/utterance)
## Data: . (Number of observations: 3367)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~minimal_pair (Number of levels: 40)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.03 0.03 0.00 0.10 1.00
## sd(vowel_n2.syllable) 0.05 0.04 0.00 0.16 1.00
## cor(Intercept,vowel_n2.syllable) -0.15 0.58 -0.97 0.94 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 3459 2620
## sd(vowel_n2.syllable) 3696 2419
## cor(Intercept,vowel_n2.syllable) 7496 2685
##
## ~minimal_pair:utterance (Number of levels: 160)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.03 0.03 0.00 0.10 1.00
## sd(vowel_n2.syllable) 0.06 0.04 0.00 0.16 1.00
## cor(Intercept,vowel_n2.syllable) -0.15 0.58 -0.97 0.92 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 3845 1967
## sd(vowel_n2.syllable) 2768 2486
## cor(Intercept,vowel_n2.syllable) 5900 2176
##
## ~speaker (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.17 0.12 0.02 0.45 1.00 1294 1381
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 0.22 0.32 -0.41 0.86 1.00 4772
## f0 -0.00 0.00 -0.00 0.00 1.00 5198
## vowel_n2.syllable -5.50 0.47 -6.45 -4.58 1.00 7666
## f0:vowel_n2.syllable 0.03 0.00 0.03 0.04 1.00 7441
## Tail_ESS
## Intercept 3116
## f0 3531
## vowel_n2.syllable 2685
## f0:vowel_n2.syllable 2696
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot(fit_f0)
conditional_effects(fit_f0,
effects = c("f0:vowel_n"))
mean_values %>%
select(speaker, utterance, stressed, minimal_pair, vowel_n, intensity) %>%
brm(stressed ~ intensity*vowel_n + (1|speaker) + (vowel_n+1|minimal_pair/utterance),
family = bernoulli(),
data = .) ->
fit_intensity##
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## Chain 4:fit_intensity## Family: bernoulli
## Links: mu = logit
## Formula: stressed ~ intensity * vowel_n + (1 | speaker) + (vowel_n + 1 | minimal_pair/utterance)
## Data: . (Number of observations: 3398)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~minimal_pair (Number of levels: 40)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.04 0.03 0.00 0.12 1.00
## sd(vowel_n2.syllable) 0.07 0.06 0.00 0.21 1.00
## cor(Intercept,vowel_n2.syllable) -0.10 0.57 -0.96 0.92 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 3202 2367
## sd(vowel_n2.syllable) 2826 2500
## cor(Intercept,vowel_n2.syllable) 5499 3215
##
## ~minimal_pair:utterance (Number of levels: 160)
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept) 0.04 0.03 0.00 0.11 1.00
## sd(vowel_n2.syllable) 0.08 0.06 0.00 0.23 1.00
## cor(Intercept,vowel_n2.syllable) -0.09 0.57 -0.95 0.94 1.00
## Bulk_ESS Tail_ESS
## sd(Intercept) 3933 2472
## sd(vowel_n2.syllable) 2721 2373
## cor(Intercept,vowel_n2.syllable) 5725 3244
##
## ~speaker (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.48 0.24 0.22 1.09 1.00 1463 1932
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -13.46 2.28 -17.93 -9.09 1.00 3567
## intensity 0.20 0.03 0.14 0.27 1.00 3601
## vowel_n2.syllable -13.24 2.61 -18.37 -7.98 1.00 3890
## intensity:vowel_n2.syllable 0.22 0.04 0.14 0.30 1.00 3908
## Tail_ESS
## Intercept 3311
## intensity 3155
## vowel_n2.syllable 3107
## intensity:vowel_n2.syllable 3080
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot(fit_intensity)
conditional_effects(fit_intensity,
effects = c("intensity:vowel_n"))
Appendix: list of stimuli
textgrids %>%
filter(tier == 1) %>%
distinct(content, source) %>%
rename(word = content,
speaker = source) %>%
mutate(speaker = str_extract(speaker, "d\\d\\d"))Нужно в IPA перевести… https://github.com/agricolamz/abaza_cyrillic_to_trans